Interpretation of aeromagnetic data using eigenvector analysis of pseudogravity gradient tensor

Geophysics ◽  
2011 ◽  
Vol 76 (3) ◽  
pp. L1-L10 ◽  
Author(s):  
Majid Beiki ◽  
Laust B. Pedersen ◽  
Hediyeh Nazi

This study has shown that the same properties of the gravity gradient tensor are valid for the pseudogravity gradient tensor derived from magnetic field data, assuming that the magnetization direction is known. Eigenvectors of the pseudogravity gradient tensor are used to estimate depth to the center of mass of geologic bodies. The strike directions of 2D geological structures are estimated from the eigenvectors corresponding to the smallest eigenvalues. For a set of data points enclosed by a square window, a robust least-squares procedure is used to estimate the source point which has the smallest sum of squared distances to the lines passing through the measurement points and parallel to the eigenvectors corresponding to the maximum eigenvalues. The dimensionality of the pseudogravity field is defined from the dimensionality indicator I, derived from the tensor components. In the case of quasi-2D sources, a rectangular window is used in the robust least-squares procedure to reduce the uncertainty of estimations.Based on synthetic data sets, the method was tested on synthetic models and found to be robust to random noise in magnetic field data. The application of the method was also tested on a pseudogravity gradient tensor derived from total magnetic field data over the Särna area in west-central Sweden. Combined with Euler deconvolution, the method provides useful complementary information for interpretation of aeromagnetic data.

Geophysics ◽  
2010 ◽  
Vol 75 (6) ◽  
pp. I37-I49 ◽  
Author(s):  
Majid Beiki ◽  
Laust B. Pedersen

We have developed a new method to locate geologic bodies using the gravity gradient tensor. The eigenvectors of the symmetric gravity gradient tensor can be used to estimate the position of the source body as well as its strike direction. For a given measurement point, the eigenvector corresponding to the maximum eigenvalue points approximately toward the center of mass of the causative body. For a collection of measurement points, a robust least-squares procedure is used to estimate the source point as the point that has the smallest sum of square distances to the lines defined by the eigenvectors and the measurement positions. It’s assumed that the maximum of the first vertical derivative of the vertical component of gravity vector [Formula: see text] is approximately located above the center of mass. Observation points enclosed in a square window centered at the maximum of [Formula: see text] are used to estimate the source location. By increasing the size of the window, the number of eigenvectors used in the robust least squares and subsequently the number of solutions increase. As a criterion for selecting the best solution from a set of previously computed solutions, we chose that solution having the minimum relative error (less than a given threshold) of its depth estimate. The strike direction of the source can be estimated from the direction of the eigenvectors corresponding to the smallest eigenvalue for quasi 2D structures. To study the effect of additive random noise and interfering sources, the method was tested on synthetic data sets, and it appears that our method is robust to random noise in the different measurement channels. The method was also tested on gravity gradient tensor data from the Vredefort impact structure, South Africa. The results show a very good agreement with the available geologic information.


2020 ◽  
Vol 1 (3) ◽  
Author(s):  
Maysam Abedi

The presented work examines application of an Augmented Iteratively Re-weighted and Refined Least Squares method (AIRRLS) to construct a 3D magnetic susceptibility property from potential field magnetic anomalies. This algorithm replaces an lp minimization problem by a sequence of weighted linear systems in which the retrieved magnetic susceptibility model is successively converged to an optimum solution, while the regularization parameter is the stopping iteration numbers. To avoid the natural tendency of causative magnetic sources to concentrate at shallow depth, a prior depth weighting function is incorporated in the original formulation of the objective function. The speed of lp minimization problem is increased by inserting a pre-conditioner conjugate gradient method (PCCG) to solve the central system of equation in cases of large scale magnetic field data. It is assumed that there is no remanent magnetization since this study focuses on inversion of a geological structure with low magnetic susceptibility property. The method is applied on a multi-source noise-corrupted synthetic magnetic field data to demonstrate its suitability for 3D inversion, and then is applied to a real data pertaining to a geologically plausible porphyry copper unit.  The real case study located in  Semnan province of  Iran  consists  of  an arc-shaped  porphyry  andesite  covered  by  sedimentary  units  which  may  have  potential  of  mineral  occurrences, especially  porphyry copper. It is demonstrated that such structure extends down at depth, and consequently exploratory drilling is highly recommended for acquiring more pieces of information about its potential for ore-bearing mineralization.


Geophysics ◽  
2012 ◽  
Vol 77 (3) ◽  
pp. F13-F20 ◽  
Author(s):  
Kamil Erkan ◽  
Christopher Jekeli ◽  
C. K. Shum

Gravity gradiometry and magnetometry methods are powerful noninvasive techniques for near-surface detection problems. Efficient integration of data from these techniques decreases the degree of nonuniqueness in geophysical interpretations. Deformation analysis is a powerful tool for comparison of two fields, which aids in this respect. We propose a fully quantitative approach, which uses the generalized theory of deformation for the geometric comparison of gravimetric and magnetic fields. The resulting deformation maps delineate regions where Poisson’s relation is violated between the two data sets and thus discriminate between air-filled cavities and other similar low-density/susceptibility geophysical sources. We present a practical corresponding algorithm that is robust in the sense that no prior knowledge of the physical properties of the subsurface is needed.


1998 ◽  
Vol 25 (19) ◽  
pp. 3721-3724 ◽  
Author(s):  
Neil Murphy ◽  
Edward J. Smith ◽  
Joyce Wolf ◽  
Devrie S. Intriligator

1972 ◽  
Vol 77 (4) ◽  
pp. 604-610 ◽  
Author(s):  
J. K. Guha ◽  
D. L. Judge ◽  
J. H. Marburger

Geophysics ◽  
2000 ◽  
Vol 65 (5) ◽  
pp. 1489-1494 ◽  
Author(s):  
Richard S. Smith ◽  
A. Peter Annan

The traditional sensor used in transient electromagnetic (EM) systems is an induction coil. This sensor measures a voltage response proportional to the time rate of change of the magnetic field in the EM bandwidth. By simply integrating the digitized output voltage from the induction coil, it is possible to obtain an indirect measurement of the magnetic field in the same bandwidth. The simple integration methodology is validated by showing that there is good agreement between synthetic voltage data integrated to a magnetic field and synthetic magnetic‐field data calculated directly. Further experimental work compares induction‐coil magnetic‐field data collected along a profile with data measured using a SQUID magnetometer. These two electromagnetic profiles look similar, and a comparison of the decay curves at a critical point on the profile shows that the two types of measurements agree within the bounds of experimental error. Comparison of measured voltage and magnetic‐field data show that the two sets of profiles have quite different characteristics. The magnetic‐field data is better for identifying, discriminating, and interpreting good conductors, while suppressing the less conductive targets. An induction coil is therefore a suitable sensor for the indirect collection of EM magnetic‐field data.


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